chdavid/rrr repository overview

⁠RRR

The Reproducible Routing Rituals (RRR) is a Python and bash shell toolbox that combines many repetitive pre and post-processing tasks that are common to studying the movements of water on and underneath the land surface. Such tasks include the preparation of files corresponding to:

• River network details (connectivity, parameters, sort, coordinates, subset)
• Contributing catchments information (area, coordinates)
• Reformatted land surface model outputs
• Coupling of LSM outputs and catchments to estimate water inflow into rivers
• Observed gauge data
• Analysis of these and associated data from a hydrological perpective

Vector-based ("blue line") river networks and associated contributing catchments can be used from the following datasets:

• The enhanced National Hydrography Dataset (NHDPlus, versions 1 and 2)
• The Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS)

Surface and subsurface runoff are obtained using model outputs from:

• The Global Land Data Assimilation System (GLDAS)
• The North American Land Data Assimilation System (NLDAS)

Water inflow from the land surface models and into the hydrographic networks are formatted for use within:

• The Routing Application for Parallel computatIon of Discharge (RAPID)

Observed gauges are gathered from:

• The National Water Information System (NWIS)

Hydrological data analysis is done for the above datasets, as well as model outputs from:

• The Routing Application for Parallel computatIon of Discharge (RAPID)

RRR is specifically designed to work hand-in-hand with RAPID. Further information on both RAPID and RRR can be found on the the RAPID website at: http://rapid-hub.org/⁠.

⁠Installation with Docker

Installing RRR is by far the easiest with Docker. This document was written and tested using Docker Community Edition⁠ which is available for free and can be installed on a wide variety of operating systems. To install it, follow the instructions in the link provided above.

Note that the experienced users may find more up-to-date installation instructions in Dockerfile⁠.

⁠Download RRR

Downloading RRR with Docker can be done using:

$ docker pull chdavid/rrr

⁠Install packages

The beauty of Docker is that there is no need to install anymore packages. RRR is ready to go! To run it, just use:

$ docker run --rm -it chdavid/rrr

⁠Testing with Docker

Testing scripts are currently under development.

Note that the experienced users may find more up-to-date testing instructions in docker.test.yml⁠.

⁠Installation on Ubuntu

This document was written and tested on a machine with a clean image of Ubuntu 16.04.1 Desktop 64-bit⁠ installed, i.e. no update was performed, and no upgrade either.

Note that the experienced users may find more up-to-date installation instructions in .travis.yml⁠.

⁠Download RRR

First, make sure that git is installed:

$ sudo apt-get install -y --no-install-recommends git

Then download RRR:

$ git clone https://github.com/c-h-david/rrr

Finally, enter the RRR directory:

$ cd rrr/

⁠Install APT packages

Software packages for the Advanced Packaging Tool (APT) are summarized in requirements.apt⁠ and can be installed with apt-get. All packages can be installed at once using:

$ sudo apt-get install -y --no-install-recommends $(grep -v -E '(^#|^$)' requirements.apt)

⁠Install Python packages

Python packages from the Python Package Index (PyPI) are summarized in requirements.pip⁠ and can be installed with pip. But first, let's make sure that the latest version of pip is installed

$ wget https://bootstrap.pypa.io/pip/2.7/get-pip.py
$ sudo python get-pip.py --no-cache-dir `grep 'pip==' requirements.pip` `grep 'setuptools==' requirements.pip` `grep 'wheel==' requirements.pip`
$ rm get-pip.py

All packages can be installed at once using:

$ sudo pip install --no-cache-dir `grep 'numpy==' requirements.pip`
$ sudo pip install --no-cache-dir -r requirements.pip

⁠Testing on Ubuntu

Testing scripts are currently under development.

Note that the experienced users may find more up-to-date testing instructions in .travis.yml⁠.